Digital pathology offers a groundbreaking opportunity to transform clinical practice in histopathological image analysis, yet faces a significant hurdle: the substantial file sizes of pathological Whole Slide Images (WSI). While current digital pathology solutions rely on lossy JPEG compression to address this issue, lossy compression can introduce color and texture disparities, potentially impacting clinical decision-making. While prior research addresses perceptual image quality and downstream performance independently of each other, we jointly evaluate compression schemes for perceptual and downstream task quality on four different datasets. In addition, we collect an initially uncompressed dataset for an unbiased perceptual evaluation of compression schemes. Our results show that deep learning models fine-tuned for perceptual quality outperform conventional compression schemes like JPEG-XL or WebP for further compression of WSI. However, they exhibit a significant bias towards the compression artifacts present in the training data and struggle to generalize across various compression schemes. We introduce a novel evaluation metric based on feature similarity between original files and compressed files that aligns very well with the actual downstream performance on the compressed WSI. Our metric allows for a general and standardized evaluation of lossy compression schemes and mitigates the requirement to independently assess different downstream tasks. Our study provides novel insights for the assessment of lossy compression schemes for WSI and encourages a unified evaluation of lossy compression schemes to accelerate the clinical uptake of digital pathology.

翻译：数字病理学为组织病理学图像分析的临床实践提供了变革性机遇，但面临一个重大障碍：病理学全切片图像（WSI）的文件体积庞大。虽然当前数字病理学解决方案依赖有损JPEG压缩来解决此问题，但有损压缩可能引入颜色和纹理差异，进而影响临床决策。先前研究分别处理感知图像质量和下游任务性能，而本研究在四个不同数据集上联合评估压缩方案的感知质量与下游任务质量。此外，我们收集了初始未压缩数据集，用于对压缩方案进行无偏的感知质量评估。实验结果表明，针对感知质量微调的深度学习模型在进一步压缩WSI时，性能优于JPEG-XL或WebP等传统压缩方案。然而，这些模型对训练数据中存在的压缩伪影表现出显著偏好，且难以泛化到不同压缩方案。我们提出了一种基于原始文件与压缩文件间特征相似度的新型评估指标，该指标与压缩WSI的实际下游任务性能高度吻合。我们的指标支持对有损压缩方案进行通用化、标准化的评估，并减少了对不同下游任务进行独立评估的需求。本研究为WSI有损压缩方案的评估提供了新见解，并推动建立有损压缩方案的统一评估框架，以加速数字病理学的临床应用。